e mpirical m ode d ecomposition based t echnique applied in experimental biosignals alexandros...
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EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE APPLIED IN
EXPERIMENTAL BIOSIGNALS
Alexandros Karagiannis
Mobile Radio Communications LaboratorySchool of Electrical and Computer Engineering
National Technical University of Athens
OUTLINE
Respiration Signal Monitoring
Empirical Mode Decomposition (EMD)
EMD based technique proposed
in this presentation
Experimental procedure - Sensor Network
Processing procedure
Experimental Results
Conclusions
2Biosignal - Respiration Signal
Standard Hospital Equipment
Miniaturized sensors
RESPIRATION MONITORING
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Acceleration Vector
Respiration Mechanism is comprised of changes in some physical quantities such as :1.Muscular motion2.Volume3.Pressure4.Flow
Muscular contraction is composed of 1.Low frequency movement related to the whole contraction (0 - 5 Hz)2.High frequency component due to vibrations (2 – 40Hz)
X,Y,Z components of acceleration vector
Acceleration
EMPIRICAL MODE DECOMPOSITIONMethod for processing nonstationary signals and signals produced by nonlinear
processes
Decomposition of the signal into a set of Intrinsic Mode Functions (IMF) which are
defined as 1. Functions with equal number of extrema and zero crossings (or at most
differed by one)2. Signal must have a zero-mean
Why Empirical Mode Decomposition?
To determine characteristic time/frequency scales for the energy
Method that is adaptive
Nonlinear decomposition method for time series which are generated by an
underlying dynamical system obeying nonlinear equations
Basic Parts of the Empirical Mode Decomposition
1. Interpolation technique (cubic spline)
2. Sifting process to extract and identify intrinsic modes
3. Numerical convergence criteria (mainly to stop the iterative process of identifying
every IMF as well as the whole set of IMFs)
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EMPIRICAL MODE DECOMPOSITION ALGORITHM
1. Local maxima and minima of d0(t) = x(t).
2. Interpolate between the maxima and connect them by a cubic spline curve. The same applies for the minima in order to obtain the upper and lower envelopes eu(t) and el(t), respectively.
3. Compute the mean of the envelopes m(t):
4. Extract the detail d1 (t) = d0(t)-m(t) (sifting process)
5. Iterate steps 1-4 on the residual until the detail signal dk(t) can be considered an IMF: c1(t)= dk(t)
6. Iterate steps 1-5 on the residual rn(t)=x(t) - cn(t) in order to obtain all the IMFs c1(t),.., cN(t) of the signal.
The procedure terminates when the residual signal is either a constant, a
monotonic slope, or a function with only one extrema.
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( ) ( )( )
2u le t e t
m t
EMPIRICAL MODE DECOMPOSITION
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Mathematical Expression of EMD processed signal
Lower order IMFs capture fast oscillation modes while higher order IMFs capture slow oscillation modes
Criteria used for Numerical Convergence
1.The sifting process ends (IMF extraction) when the range of the mean of the envelopes m(t) is lower than 1‰ (0.001) of Ci (Candidate IMF)
2.Iteration process ends when the residue r(t) is 10% or lower of the d(t)
IMF set residual
EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE APPLIED ON BIOSIGNALS
Basic Idea1. Partial Signal Reconstruction by appropriate selection of IMFs and exclusion of those
IMFs that contribute to the noise contamination and feature distortion of the signal.2. Some IMFs of the produced IMF set may have a physical meaning while other IMFs don’t
have a physical meaning.
How can we select the appropriate IMFs?Application of criteria on each IMF based on the spectral characteristics (frequency ,power) of each IMF corresponding to the spectral characteristics of the signal that we wish to delineate. Decision stage for IMF selection or exclusion.
Determination of spectral characteristics of
experimental signals1. Literature documentation 2. Statistically identify the spectral content of the experimental signals
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EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON
BIOSIGNALS
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Apply spectral criteria on i-th IMF
EMD processed Experimental Respiratory Signal
EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON
BIOSIGNALS
Experimental Procedure
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Sink
Motes interfacing with sensors
Motes relay to the sink
Wireless Sensor Network
Respiration signal sampled from the mote
Respiration imported for processing
EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON
BIOSIGNALS
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1. Analog 2-axis Accelerometer
Experimental Setup
2. Multichannel Sampling of X, Y axes.Data are packed in one Radio message and transmitted
Channel 1
Channel 2 (X axis)
Channel 3 (Y axis)
00 FF FF FF FF 10 00 03 00 00 05 07 07 EB 06 0B 05 1F 07 E7 05 FF AC 4B
ADC0 ADC1 ADC2 ADC10 ADC11 ADC12 TimestampmoteIDDestination Address
Source Address
GroupID handler
3. Code developed in TinyOS-NesC oriented for event driven applications.
EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNAL
Processing Procedure1. Respiration signals were monitored in X,Y axes by measuring the
acceleration
2. Application of the EMD on each axis signal
3. Application of the spectral criteria on each IMF of 2-axes respiratory signal
3. Evaluation of the EMD based technique was aided by metrics computation (Cross Correlation Coefficients)
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data
EMD
Set of IMF
Apply Spectral Criteria on the IMF set
Select IMF
Partial Signal Reconstruction
Metric for overall performance
Application of EMD based technique in both X,Y axes signal from the 2-axis
accelerometer.
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EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNAL
Original Y axis signal
Lower order IMFs
Higher order IMFs
Residual signal
EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNALS
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1. Decision Stage for the selection of appropriate IMFs computes the mean power of the N max power peaks in order to have a smoother estimate and more precise view of the power spectral density of each IMF
2. Axis components (X,Y,Z) magnitude is closely related to the measurement point selection
3. Y axis component is significantly higher compared to X axis component in measurement point 1 and the opposite stands for measurement point 2 X,Y axes components.
Experimental Results
EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNALS
Experimental Results
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1. Adaptive power threshold criterion (based on the max mean power and minimum mean power of each IMF) produces a smaller number of IMFs suitable for partial signal reconstruction. Rigid power thresholds (based on the minimum of mean power of all IMFs) produce greater IMF set.
2. Different frequency ranges and power thresholds result in different IMF sets.
3. IMF sets produced by the adaptive power threshold stage suitable for partial signal reconstruction have smaller correlation with the original axis signal without compromising the characteristics of the signal. (Trade Off)
EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNALS
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Experimental Results 1. High frequency denoising due to removal from the IMF set of the lower order IMFs is accomplished without altering the characteristic
attributes of the signal2. Adaptive power threshold stage is more effective in filtering after the
partial signal reconstruction rather than rigid power thresholds. This is due to the smaller IMF sets.
Measurement point 2
X axis
Measurement point 2 Y axis
EMPIRICAL MODE DECOMPOSITION BASED TECHNIQUE ALGORITHM APPLIED ON BIOSIGNALS
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Conclusions1. Empirical Mode Decomposition based technique that utilize the
decomposition of the signal to IMFs in order to apply a Partial Signal Reconstruction process
2. The proposed technique tries to identify and use at the partial signal reconstruction stage those IMFs that may have a physical meaning.
3. Two stage process of the technique – Decision based on the spectral characteristics of the IMFs (frequency, power)
4. IMFs that satisfy conditions (frequency criterion, power criterion) are considered for Partial Signal Reconstruction. The others are excluded.
5. Different conditions set by the criteria produce different IMF sets for the Partial Signal Reconstruction
6. Mode mixing problem does not affect significantly the decision stage because of the disparate scales of the IMFs of the EMD processed respiratory signals.
7. EMD demands high computational and memory resources. A preprocessing stage prior to the application of the technique reduce time and resource demands without compromising signal quality
8. Future work : MIT-BIH records to apply the technique, lung sounds, Weighed Partial Signal Reconstruction, Implementation on sensor network node level .